While current methods have shown promising progress on estimating 3D human motion from monocular videos, their motion estimates are often physically unrealistic because they mainly consider kinematics. In this paper, we introduce Physics-aware Pretrained Transformer (PhysPT), which improves kinematics-based motion estimates and infers motion forces. PhysPT exploits a Transformer encoder-decoder backbone to effectively learn human dynamics in a self-supervised manner. Moreover, it incorporates physics principles governing human motion. Specifically, we build a physics-based body representation and contact force model. We leverage them to impose novel physics-inspired training losses (i.e., force loss, contact loss, and Euler-Lagrange loss), enabling PhysPT to capture physical properties of the human body and the forces it experiences. Experiments demonstrate that, once trained, PhysPT can be directly applied to kinematics-based estimates to significantly enhance their physical plausibility and generate favourable motion forces. Furthermore, we show that these physically meaningful quantities translate into improved accuracy of an important downstream task: human action recognition.

当前方法在从单目视频中估计3D人体动作方面取得了有希望的进展，本文提出了一种考虑物理学规律的预训练变压器（PhysPT），改进了基于运动学的动作估计并推断了运动力。实验证明，经过训练后的PhysPT能够直接应用于运动学估计，显著增强其物理上的可信度并产生有利的运动力，这些有意义的物理量进一步提高了重要下游任务——人体动作识别的准确性。

PhysPT：从单眼视频估计人体动力学的物理感知预训练变压器